# Applications of Deep Learning (WUSTL, Spring 2017) — Data

来源：https://www.kaggle.com/competitions/applications-of-deep-learning-wustl-spring-2017/data

本地 Meta Kaggle 快照；不是本次实时页面。

# Dataset Description

<p>This page shows up underneath the data files. Here you describe what files you have provided and the format of each. There is no single format for this page that is appropriate for all competitons, but you should strive to describe as much as you can here. A little time spent describing the data here can save a lot of time answering questions later.</p>
<p>Participants should be able to answer questions like these after reading the data description: What files do I need? What should I expect the data format to be? What am I predicting? What acroynms will I encounter?</p>
<h2>File descriptions</h2>
<ul>
<li><strong>train.csv</strong> - the training set</li>
<li><strong>test.csv</strong> - the test set</li>
<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>
</ul>
<h2>Data fields</h2>
<ul>
<li>id - An ID that is unique to each row.</li>
<li>title - The title to the Wikipedia article.</li>
<li>len - The length of the Wikipedia article.</li>
<li>cite_arXiv - Count of arXiv citations in article.</li>
<li>cite_av_media - Count of AV media citations in article.</li>
<li>cite_book - Count of book citations in article.</li>
<li>cite_comic - Count of comic book citations in article.</li>
<li>cite_conference - Count of conference citations in article.</li>
<li>cite_encyclopedia - Count of encyclopedia citations in article.</li>
<li>cite_episode - Count of episode citations in article.</li>
<li>cite_govtrack - Count of govtrack citations in article.</li>
<li>cite_journal - Count of journal citations in article.</li>
<li>cite_magazine - Count of magazine citations in article.</li>
<li>cite_press_release - Count of press release citations in article.</li>
<li>cite_web - Count of web citations in article.</li>
<li>links - Count of links to other wikipedia articles.</li>
<li>tables - Count of tables in article.</li>
<li>files - Count of files (e.g. images) in article.</li>
<li>math - Count of math expressions in article.</li>
<li>class - The class of the article (this is the target), these have the following meaning:</li>
<ul>
<li>0 is a company</li>
<li>1 is a politician</li>
<li>2 is a scientist</li>
<li>3 is a university</li>
<li>4 is an automobile</li>
<li>5 is a type of dog</li>
</ul>
</ul>